Empower Decision Makers with SAP Analytics Cloud: Modernize BI with SAP's Single Platform for Analytics

By Vinayak Gole and Shreekant Shiralkar

Discover the capabilities and features of SAP Analytics Cloud to draw actionable insights from a variety of data, as well as the functionality that enables you to meet typical business challenges. With this book, you will work with SAC and enable key decision makers within your enterprise to deliver crucial business decisions driven by data and key performance indicators. Along the way you’ll see how SAP has built a strong repertoire of analytics products and how SAC helps you analyze data to derive better business solutions.

This book begins by covering the current trends in analytics and how SAP is re-shaping its solutions. Next, you will learn to analyze a typical business scenario and map expectations to the analytics solution including delivery via a single platform. Further, you will see how SAC as a solution meets each of the user expectations, starting with creation of a platform for sourcing data from multiple sources, enabling self-service  for a spectrum of business roles, across time zones and devices. There’s a chapter on advanced capabilities of predictive analytics and custom analytical applications. Later there are chapters explaining the security aspects and their technical features before concluding with a chapter on SAP’s roadmap for SAC.

Empower Decision Makers with SAP Analytics Cloud takes a unique approach of facilitating learning SAP Analytics Cloud by resolving the typical business challenges of an enterprise. These business expectations are mapped to specific features and capabilities of SAC, while covering its technical architecture block by block.

What You Will Learn

• Work with the features and capabilities of SAP Analytics Cloud
• Analyze the requirements of a modern decision-support system
Use the features of SAC that make it a single platform for decision support in a modern enterprise.
• See how SAC provides a secure and scalable platform hosted on the cloud 

Who This Book Is For

Enterprise architects, SAP BI analytic solution architects, and developers.

• Language: English
• Publisher: Apress
• Release date: Sep 28, 2020
• ISBN: 9781484260975

Book preview

© Vinayak Gole, Shreekant Shiralkar 2020

V. Gole, S. ShiralkarEmpower Decision Makers with SAP Analytics Cloudhttps://doi.org/10.1007/978-1-4842-6097-5_1

1. Current Trends in Analytics and SAP’s Road Map

Vinayak Gole¹  and Shreekant Shiralkar²

(1)

Mumbai, India

(2)

Mumbai, Maharashtra, India

Running a successful enterprise depends on the ability to gain insight into enterprise data and to extract and present information in a meaningful way. A company’s employees need to be able to transform data into actionable insights no matter where they are located in the enterprise.

Business Intelligence

Let us appreciate how and why Business Intelligence (BI) is essential for an Intelligent Enterprise. At the beginning of the century, while technology was proliferating across enterprise functions and its ubiquitous nature was fueling a data explosion, the availability of data with its variety at huge velocity gave birth to concepts like Big Data. Figure 1-1 is a reflection on how BI gained center stage.

../images/496704_1_En_1_Chapter/496704_1_En_1_Fig1_HTML.jpg

Figure 1-1

Triggers and Impact: Analytics and Business Intelligence

Technological invention brought down the cost of BI technology whether it was for sourcing the data or storage or processing the data, paving the way to the phenomenon and fact that Data is the new oil. Alongside value derivation from data, data monetization started to gain attention and gave motivation to associated fields like data science and advanced analytics including predictive analytics. Figure 1-2 summarizes the value from BI.

../images/496704_1_En_1_Chapter/496704_1_En_1_Fig2_HTML.jpg

Figure 1-2

Value from BI

Not long ago, analytics was a domain limited to IT and data analysts, who supported the decision-makers by delivering data visualizations. In the digital economy, self-service has become the norm that mandates software solutions to be intuitive and easily adopted and used by the regular business user across the enterprise; and there’s a trend for the end user to learn the solution, understand the data models, and manage the content. Artificial intelligence technologies embedded within analytical applications have the potential to be able to make operational decisions without much human intervention. Analytical applications will augment end users to perform complex analytical tasks with algorithms, machine learning, and new natural language processing-based voice interfaces.

Applications and the use of analytics added developments to self-service, analytics specific to an industry like retail, and specific functions, for example, advertising. Planning functions, due to their similarity with technical uses and applications, started getting integrated with analytic solutions and offerings. Lastly but equally important, business changes were needed to reduce the time and cost of deployment of the analytical solution.

On one hand, all of today’s digital economy is becoming progressively dependent upon infrastructure such as hardware, software, telecoms, networks, etc.; and more and more business is conducted over computer-mediated networks and commerce is transacted over the internet – for example, transfer of goods or legal contracts. On the other hand, software solutions are also undergoing a major transformation with increased computational capacity and new ways to source more data that fuels machine learning (ML) and artificial intelligence (AI). These have essentially made SAP review their analytics portfolio and make it compelling for the digital economy.

In view of the aforesaid trends and developments, all the leading IT product companies had started to review their portfolio and focus on solutions for management of data as well as processing capabilities to generate insights. Some examples are shared next.

Microsoft Corporation acquired ProClarity Corp. in 2006 for adding advanced analysis and visualization technologies; and in 2008 it acquired DATAllegro Inc. for strengthening its MS-SQL Server with flexible software architecture and more recently in 2015 acquired Revolution Analytics R-based analytic solutions that can scale across large data warehouses and Hadoop systems and can integrate with enterprise systems. Similarly, International Business Machines Corporation aka IBM acquired Informix in 2001 and added the capability for high-performance domain-specific queries and efficient storage for datasets based on semi-structured data, time series, and spatial data. In 2004 they acquired Alphablox, Cognos in 2008, Netezza in 2010, and StoredIQ in 2012 to ensure their portfolio of solutions remained competitive and relevant for their clients.

In recognition of the opportunity and retaining its leadership in analytics solutions, SAP acquired BusinessObjects (BO) in 2007, the leading product then in the BI space. SAP further consolidated its position by acquiring Sybase in 2010 while introducing HANA, a new generation of an in-memory database with MPP capability. In 2013 SAP acquired KXEN, a leading product in predictive analytics with self-service features. The portfolio was further enriched by acquisition of RoamBi and Altiscale in 2016 to fill the whitespaces of mobile access to analytics and big data as a service offering.

The Evolution of SAP’s Portfolio

With acquisitions, SAP’s portfolio and offerings for analytic solutions had ballooned; and aside from causing confusion to clients and SI partners, its maintenance and sales were also challenged. SAP therefore started to rationalize its solutions and offerings. Following is an overview of the evolution of SAC within SAP’s portfolio of solutions for BI requirements:

Post-acquisition of BO, SAP engaged in integrating it within its portfolio and around 2010 started to present its solutions for specific BI tasks like Crystal Reports for reporting, Xcelsius for dashboards and visualization, and later – around 2012 – recommended its clients to start using SAP BO Design Studio instead of Bex Web Application Designer.

With the introduction and successful adoption of HANA enabling advanced analytical applications, SAP started to redevelop all its solutions keeping HANA as the base for all its solutions. After acquiring Sybase and KXEN, around 2015, SAP recognized the need to converge its SAP BI client portfolio by announcing that SAP Lumira would become the mainstay for data discovery and analysis, while Design Studio would become the mainstay for all dashboard and analytical application development.

In early 2015, SAP introduced SAP Cloud for Planning, and with its success, along with a rich road map, it evolved into SAP Cloud for Analytics and later rebranded to SAP Analytics Cloud.

Figure 1-3 depicts the evolution of SAC.

../images/496704_1_En_1_Chapter/496704_1_En_1_Fig3_HTML.jpg

Figure 1-3

Evolution of SAP Analytics Cloud

SAP Analytics Cloud (SAC) is a single solution as a best-in-class Software-as-a-Service (SaaS) solution that combines all the analytics functionalities (planning, predictive, business intelligence) in one intuitive user interface, saving time and effort while making better decisions.

On the success of its ERP, SAP has been focusing on enabling enterprises to empower business users to make effective business decisions. In the digital economy, there is increased computational capacity with new ways to source more data. Managing, processing, or abundance of data is plausible by application of machine learning (ML) and artificial intelligence (AI); hence, alongside the transformation of its software, SAP was pushed to overhaul its offering for analytic challenges of the digital economy.

SAP has been at the forefront of the Enterprise Technologies and identified its offering for Intelligent Enterprise in three building blocks, namely Intelligent Suite consisting of the Digital Core (read S/4HANA), CRM, SRM, and suite of SAP products; the Digital Platform consisting of Data Management and Cloud Platform; and the Intelligent Technologies consisting of Machine Learning (ML), IoT, and Analytics. Business intelligence is a key component of the Intelligent Technologies.

Having learned about the positioning of analytics as one of the three pillars of SAP’s offering for Intelligent Enterprise, let’s explore capabilities of SAC that support the processes of an Intelligent Enterprise.

SAP developed SAC with the objective of offering a single solution for business intelligence and collaborative business planning. SAC delivers data discovery capability, be it on the data sources on premise or in the cloud, without moving, caching, or persisting any part or portion of the data into the cloud. The solution is built for the cloud and complemented with predictive analytics and machine learning technology. With SAC, SAP has unified the core domains of BI, planning, and is complemented by predictive analytics to deliver new capabilities such as simulation in BI, storytelling in planning, predictive forecasts in planning, or automated discovery in BI. The complementary capabilities from predictive and planning, on one hand, enable BI to shift from visualizing data to actually working with insights through ad hoc simulations, testing hypotheses, and planning for the future. On the other hand, it enables users to configure formulas for different accounts, manage currency conversion tables, as well as allocating values in planning.

Prior to SAC, SAP had multiple solutions in the portfolio (refer to Figure 1-4); and then around 2018, SAC became the primary solution combining capabilities of multiple solutions, namely SAP BO Explorer, SAP Roambi, SAP Lumira and SAP Analysis for OLAP.

../images/496704_1_En_1_Chapter/496704_1_En_1_Fig4_HTML.jpg

Figure 1-4

Rationalization of SAP BI portfolio

Let’s learn to appreciate a typical business situation in which a business user has to refer to multiple applications in order to achieve the desired analysis or planning, for instance, of emails, messenger services, ERP data, and business intelligence, among others. For example, in the planning process, the user may begin by reviewing historical and present data as a starting point possibly through an analytical report. The user then applies multiple formulas to generate forecasts and what-if scenarios, possibly done in a separate predictive analytical solution. After coming up with preliminary numbers, those are shared with management or other stakeholders for their review and approval; most often, sharing is done over a collaboration tool or email or messenger, and later they are presented or published to all the stakeholders. The situation described above showcases, how a user has to refer to multiple applications for sourcing and processing the information before it is published to the stakeholders, and that too while relying on the IT unit to support each of the tasks and processes before the output, can be meaningful for the relevant task or process.

Highlights of SAC Capabilities

In this section, we will learn about specific capabilities of SAC with underlying technical components and features. Figure 1-5 depicts capabilities of the SAP Analytics Cloud. We will briefly cover the All-in-One Platform, Augmented Analytics, Single Version of Truth, Anytime Available, Predictive Analytics, and Custom Analytics Application Design.

../images/496704_1_En_1_Chapter/496704_1_En_1_Fig5_HTML.jpg

Figure 1-5

SAP Analytics Cloud - Capabilities

All-in-One Analytics Platform

With analytics forming a core pillar in SAP’s Intelligent Enterprise framework, SAP’s design view for SAC’s architecture has been to promote a single platform based on HANA to encompass analytics, planning, and predictive analytics. With a view to eliminate referring to multiple applications, SAP brought together the capabilities of Analytical Solution and Planning in SAC as a single solution that enables performing each of the multiple tasks and process in the business in an effortless way, without being dependent on the IT unit. SAC is also the focus for SAP innovation and sees regular updates in terms of new technologies being rapidly incorporated into the platform. SAC is also native to the cloud, built from scratch on the SAP cloud platform that enables access to analytical functions from multiple devices. These features make SAC the tool of choice for bringing all analytics onto a single platform. Some of the benefits that SAC offers for bringing all analytics to a single platform are the following:

a.

HANA as the foundational platform: SAC is built on the HANA Cloud platform. Having a versatile platform on the cloud with a proven database technology enables SAC to be equally flexible in terms of approaching analytics holistically. The HANA platform brings to the table robust data processing skills and new age storage technologies. Building on this framework, SAC delivers a complete packaged set of analytics functions that can be based out of a single data processing layer.

b.

Faster insight to action: SAC’s robust collaboration tools between end users and connectivity between tools ensure less time is spent on discussions and analysis of results. Decisions can be arrived at quickly with traceability within the system itself.

c.

Lower Cost of Ownership: With the capacity to integrate multiple functions related to analytics onto a single platform, HANA provides the data processing capabilities, and the application layer of SAC brings together disparate functions. One of the primary benefits of a single platform is the total cost to the organization in terms of license and infrastructure. SAC’s SaaS billing options ensure there is rapid scalability available whenever needed.

Augmented Analytics

SAC utilizes machine learning technology intrinsic to the underlying platform for delivering augmented analytics capabilities and uses techniques such as data mining, statistical modeling, and machine learning to present the end user with a forecasted value based on historical data. The augmented analytics capabilities of SAC are collectively known as Smart Assist.

a.

Faster insights to action: The augmented analytics of SAC enable end users to build stories using machine language technologies and converse with the analytics application in a natural language. The ease of exploring data brought about by Smart Discovery and Search to the Insight feature enables end users to focus on specific data points and explore data contextually with domain knowledge. Predictive Forecast and Smart Insights allow end users to rapidly convert analytics into actionable insights by creating a plan with the focus on forecasted values.

b.

Low learning curve: SAC enables the use of modern advanced analytics and derive all the advantages without having to delve deeper into the understanding of machine learning and statistical models. Augmented analytics enable end users to include forecasts and simulations directly into their stories. Stories can be presented to management through the single SAC platform. With the low learning curve, end users can save time, avoid redundancy, and make the best use of available data to make data-driven decisions.

c.

Reduced redundancy: SAC’s Smart Discovery and Smart Insight features, when combined with the powerful storytelling capability, enable end users to build focused reports and presentations. Executive dashboards can be built directly over digital boardrooms, which allow for real-time data exploration. Deeper insights are presented by Smart Insights.

Single Version of Truth

SAC is built on the SAP HANA platform and is part of SAP Cloud solutions. SAP Cloud delivers multiple cloud-based solutions and services integrated into a single platform, allowing customers to integrate across multiple data sources including big data and streaming data, enabling a platform for Single Version of Truth. SAC’s connections and modeling tools allow businesses to deploy a single tool for data exploration of all data sources. SAP sources also allow for live connectivity as well as seamless data flow across objects such as hierarchies.

a.

Low Data Footprint:

The modeler in SAC enables data wrangling and blending. Heterogenous data can be rapidly transformed to provide end users with a strong semantic layer in the form of models. Models eliminate the need to store and process data across silos over multiple layers.

b.

Rapid Deployment:

SAP has extended its best practices and standard business content to the SAC environment as well. SAP delivers business content across lines of business as well as across industry best practices. Out of the box, these content packages can be installed directly over the content delivery network and be rapidly deployed with minimal alterations.

c.

Quick Scaling:

Enterprise Analytics Applications have traditionally been driven by licensing. Scaling up or down in terms of capacity is a major challenge for system owners to enable multiple layers of end users to be onboarded to these applications. SAC, being a cloud native application, allows rapid scaling in terms of capacity as well as users to the existing landscape.

Anytime Available

SAC is built on a cloud native solution, that is, built from the ground up on the SAP Cloud platform and maintained by SAP, ensuring around-the-clock access to data for analysis without downtimes.

a.

Native to Cloud: SAC is a cloud native application built over the high-performance SAP HANA Cloud platform. The entire application can be accessed through a web browser without the need for installing a local application. Since there is no local installation, SAC can be accessed from a device with any OS or version. The browser also enables debugging of the application intrinsically without the need for an additional application. Also, the entire landscape, including regular updates, is maintained by SAP and the enterprises do not have to spend time and effort in upgrading.

b.

Mobile apps: SAC provides a mobile app for both of the most popular mobile platforms, viz, iOS as well as Android. The iOS app has been available and has matured over the years whereas the Android app has been launched in Q1 2020.

c.

Content Network: SAP provides pre-built out-of-the-box content for most Lines of Business (LOB)s and industries, which can be rapidly deployed through the Content Network. This enables end users to rapidly build and consume dashboards and stories from any location without the need to set up a separate development.

Predictive Analytics

SAP has integrated predictive capability within the SAC, allowing end users to create predictive scenarios and integrating those scenarios into analytical and planning stories. SAC delivers a custom-built predictive analytics capability solution with Smart Predict. Here are some of the benefits offered by Smart Predict:

a.

Informed Decisions: SAC’s Smart Predict technologies enable not only statistical analysis but also provide end users with an easy-to-use explainable technology tool, with a low learning curve. Enabling end users with Regression for predicting numerical values, Classification for binary decisions, and Time Series for forecast over time, informed decisions can help organizations plan for the future.

b.

Reduced Risk: Smart Predict enables automated analysis of historical data with simple explainable technologies to enable end users to make informed decisions. Since the decisions are now driven by data and not on past experience, the probability of a disruption is very minimal, thus considerably reducing the risk involved.

c.

Improved Business outcomes: With business plans targeted specifically for signal values based on forecasts, investments can be made specific to business lines and products, ensuring business outcomes. With specific predictive scenarios, organizations can drive better outcomes for business plans and improved investment results.

Custom Analytics Application Design

SAC enables building custom analytic applications. The custom analytic applications cater to specific requirements while relying on complex scripting to deliver a best-of-the-class experience to end users. Allowing customizations from data connectivity, discovery as well as user experience, while continuing to easily integrate with the available functionality like BI and planning, SAC delivers a complete package for end-to-end analytics.

a.

Flexibility: SAC Analytic Applications ensure flexibility in terms of building applications where the standard reports and stories fail. With simple JavaScript-based scripting, the SAP Analytics Designer is able to fulfill most of the custom features to be built into the application. It also enables multiple components and widgets that can be placed onto the canvas and scripted as per requirements. Integrating into the existing architecture, the Analytic Applications display the utmost flexibility in terms of features.

b.

Reusability: The components or widgets built within the canvas layout can be published as reusable components for use within the entire enterprise SAC landscape. SAC Analytics Designer also allows for the development of custom themes that can also be published for consumption. A considerable amount of time can be saved by reusing components. The SAC Analytics Designer also enables creating a new custom color palette that can be reused across other reports, stories, and applications. Custom CSS can also be included to enable further reusability within the SAC Application. This is especially useful when the organization has defined a set of colors for its brands as well as for the enterprise. Development effort can be reduced while increasing the efficiency of delivery of the applications to the end users by enabling reusability across the components and widgets.

c.

Insights to action: SAC enables programmatic flexibility into the traditional information dashboard by embedding data actions into the dashboard. SAC thus enables Insights to Action within the landscape itself. This feature is especially useful for maintaining consistency and standards and enable a complete 360-degree execution for data actions. SAC’s Analytics Designer offers a custom development environment that previously was available only with Lumira Designer and SAP Design Studio. Offering a complete custom development package, the Analytic Designer enables building applications to be built using connections to data sources or through file uploads. The Analytics Designer can be integrated with other components of the SAC like Planning, Stories, and Connections, as well as other web applications.

Further, SAC has an intuitive interface that enables a business user to perform tasks with ease, through functionality that looks and feels a lot like the tools they are accustomed to, thereby facilitating a faster adoption.

Spreadsheets without their typical challenges such as multiple copies or performance issues since the solution uses the SAP HANA Cloud Platform.

Mere selection of a range of cells and SAC proposes a most apt visualization for that data, along with multiple graphic options.

Sharing specific files, reports, or even cells of information with other users to discuss and create an action plan based on that discussion.

Managing a calendar of events that will help streamline the entire process.

Publishing presentations with the final information.

SAP's main focus has been in removing complexity and providing an all-in-one application, so it has therefore been continually enhancing SAC with many more such intuitive interface options that help companies make the most from their investment in SAC with the least time for adoption.

In summary, SAC is a Saas offering from SAP that combines the analytics and planning capabilities into a single solution. SAC consolidates data from all the applications and presents smart analytics for forecasting and what-if simulations through an intuitive and role-based interface. Analysts and planners in all lines of business are empowered with deep insights and data-based decision-making to act on issues collaboratively from anywhere.

Table 1-1 provides an overview of SAC capabilities mapped to underlying technical details.

Table 1-1

Summary of SAC Capabilities and Its Technology